System and method for real-time transactional data obfuscation

ABSTRACT

A system and method for providing transactional data privacy while maintaining data usability, including the use of different obfuscation functions for different data types to securely obfuscate the data, in real-time, while maintaining its statistical characteristics. In accordance with an embodiment, the system comprises an obfuscation process that captures data while it is being received in the form of data changes at a first or source system, selects one or more obfuscation techniques to be used with the data according to the type of data captured, and obfuscates the data, using the selected one or more obfuscation techniques, to create an obfuscated data, for use in generating a trail file containing the obfuscated data, or applying the data changes to a target or second system.

CLAIM OF PRIORITY

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 61/369,000, titled “SYSTEM AND METHOD FORREAL-TIME TRANSACTIONAL DATA OBFUSCATION”, filed Jul. 29, 2010; whichapplication is herein incorporated by reference.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD OF INVENTION

The present invention is generally related to computer transactions, andis particularly related to a system and method for providingtransactional data privacy while maintaining data usability, includingthe use of different obfuscation functions for different data types tosecurely obfuscate the data, in real-time, while maintaining itsstatistical characteristics.

BACKGROUND

New data privacy laws have appeared recently, such as the HIPAA laws forprotecting medical records, and the PCI guidelines for protecting creditcard information. Data privacy can be defined as maintaining the privacyof Personal Identifiable Information (PII) from unauthorized accessing.PII includes any piece of data that can be used alone, or in conjunctionwith additional information, to uniquely identify an individual.Examples of such information include national identification numbers,credit card numbers, as well as financial and medical records. Accesscontrol methods and data encryption provide a level of data protectionfrom unauthorized access. However, it is not enough—for example, it doesnot prohibit identity thefts. It was recently suggested that 70% of dataprivacy breaches are internal breaches that involve an employee from theenterprise who has access to some training or testing database replica,which contains all the PII. Accordingly, in addition to access control,what are needed are techniques to protect such datasets, includingpreserving the data usability while protecting its privacy. Thesechallenges are further complicated when realtime requirements are added.This is the general area that embodiments of the invention are intendedto address.

SUMMARY

Described herein is a system and method for providing transactional dataprivacy while maintaining data usability, including the use of differentobfuscation functions for different data types to securely obfuscate thedata, in real-time, while maintaining its statistical characteristics.In accordance with an embodiment, the system comprises an obfuscationprocess that captures data while it is being received in the form ofdata changes at a first or source system, selects one or moreobfuscation techniques to be used with the data according to the type ofdata captured, and obfuscates the data, using the selected one or moreobfuscation techniques, to create an obfuscated data, for use ingenerating a trail file containing the obfuscated data, or applying thedata changes to a target or second system.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates an environment or system which can use dataobfuscation, in accordance with an embodiment.

FIG. 2 illustrates the use of a GT-ANeNDS algorithm, in accordance withan embodiment.

FIG. 3 illustrates histogram decomposition for numerical data, inaccordance with an embodiment.

FIG. 4 illustrates a function for obfuscating identifiable numericaldata, in accordance with an embodiment.

FIG. 5 illustrates a table of possible data-types and semantics, andwhich technique the system can use to obfuscate each data type, inaccordance with an embodiment.

FIG. 6 illustrate the results of the K-mean algorithm on original data,in accordance with an embodiment.

FIG. 7 illustrate the results of the K-mean algorithm on obfuscateddata, in accordance with an embodiment.

FIG. 8 illustrates examples of original and obfuscated values, inaccordance with an embodiment.

DETAILED DESCRIPTION

Data privacy is no more an optional feature—it is a requirement by anydata management system to preserve the privacy of the data of the usersof the system. Recently, new privacy laws have appeared, such as theHIPAA laws for protecting medical records, and the PCI guidelines forprotecting credit card information. Data privacy (also referred to asinformation privacy) can be defined as maintaining the privacy ofpersonal identifiable information or data from unauthorized accessing.Data privacy refers to developing relationship and interaction betweentechnology and the privacy of personally identifiable information (PII)that is collected, stored, and shared by organizations. PII includes anypiece of data that can be used alone, or in conjunction with additionalinformation, to uniquely identify an individual. Examples of suchinformation may include first and last names, social security numbers,national identification numbers, addresses, date of birth, phonenumbers, email addresses, driver's license numbers, credit card numbers,financial, and medical records.

Data security has generally been provided through access control.Although access control methods provide a level of data protection, itis not enough. Access control methods, in addition to data encryption,protect data from unauthorized access. However, it does not prohibitidentity thefts. It was recently suggested that that 70% of data privacybreaches are internal breaches that involve an employee from theenterprise who has access to some training or testing database replica,which contains all the PII. Therefore, there is a need for techniquesthat would prevent such identity thefts. Ideally, what is needed is atechnique that would protect the PII from unauthorized access, and allowaccess for analysis, testing and training purposes, while maintainingits usability. The challenge here is the contradicting requirement of ausable copy of the data that, yet, does not breach the privacy of thedata. Examples of systems that can benefit from these requirements mayinclude large financial credit card enterprises.

Data Obfuscation

As referred to herein, Data Obfuscation (DO) is a broad term that refersto any data manipulation technique used to induce ambiguity to the data,desensitize it to be of no sense, yet usable, and thus preserving itsprivacy. The main requirements of a DO technique are data privacy andusability. Data privacy refers to the fact that the PII are secured andconcealed upon applying the DO technique to the data. Usability refersto the fact that the transformed data is still useful and maintains themain statistical and semantic properties of the original data. Inaddition, there are a set of desired properties, such as:

-   -   1. Providing access to the confidential attributes should        provide the intruder with no additional information. In other        words, the ability to predict the original data given access to        the obfuscated data should never be possible to use it to        retrieve the original sensitive data given the technique and the        obfuscated data.    -   2. The DO technique should be irreversible. It should never be        possible to use it to retrieve the original sensitive data given        the technique and the obfuscated data.    -   3. Semantics and referential integrity must be maintained.    -   4. Obfuscation must be a repeatable process to guarantee        consistency. This means that every time a data item is being        obfuscated, it is obfuscated to the same obfuscated data item.

All of these requirements make the task of obfuscating the dataefficiently a substantial challenge, which is even more challenging whenrealtime requirements are added as in the motivating example below.

Consider the case when a software-based data replication product, suchas Oracle GoldenGate, is used to replicate bank transactional dataacross heterogeneous sites, where one copy of the data is replicated toa third party site to be used for real-time analysis purposes, say forfraud detection for instance. One way to do so is to replicate the data,and then apply an existing obfuscation technique in an offline fashionand then use the obfuscated copy for analysis. Note that a mappingbetween original and obfuscated data items is needed in this example.This can be maintained securely encrypted at the original data host.This solution, although relatively simple, does not satisfy thereal-time requirements of the fraud detection. In addition, a copy ofthe original data is being copied and stored at a third party sitebefore it is being obfuscated, which is a huge security threat. Thus, aneed for a real-time transactional data obfuscation technique is needed:a technique that satisfies all desired properties of obfuscationtechniques, in addition to satisfying the real-time requirements.

As disclosed herein, in accordance with an embodiment a system andmethod is disclosed for providing a transactional data obfuscationsolution, which can be used, e.g. with software-based data replicationproducts such as Oracle GoldenGate. In accordance with an embodiment,the system utilizes different obfuscation functions for different datatypes to securely obfuscate, on real-time, the data while maintainingstatistical characteristics of the data, for testing and analysispurposes.

Many techniques have been proposed for data privacy such as: (1) datarandomization, which adds noise to the data; (2) data anonymization,which uses generalization and suppression to make the data ambiguous;(3) data swapping, which involves ranking data items and swappingrecords that are close to each other; (4) geometric transformation,which uses transformations such as rotation, scaling, and translationfor distorting the data; and (5) nearest neighbor data substitution,which uses Euclidean distance to define neighbors, and then performswapping. Some of these techniques apply to only certain data types; forexample, geometric transformation techniques apply only to numericaldata. The majority of these techniques were developed for privacyprotection for data mining and analysis, for which there are noreal-time requirements. To the inventors knowledge, all these techniquesinvolve an offline analysis phase, at which the statisticalcharacteristics of the data set is captured, and used to guide theobfuscation, in order to maintain these statistical characteristics.

In some environments, such as with a software-based data replicationproduct such as Oracle GoldenGate, transactional data is beingreplicated on real-time fashion, and hence, a real-time obfuscationtechnique is needed. In accordance with an embodiment, the systemprovides a suite of techniques for obfuscating different data types. Forexample, for numerical data, a technique is proposed herein that isbased on both geometric transformation, namely GT-NeNDS, andanonymization. These two techniques are described in more detail below.

GT-NeNDS is a numerical data obfuscation technique that is designed forclustering mining. In accordance with an embodiment, GT-NeNDS isextended to make it applicable in real-time, by applying anonymization,which adds to the data privacy, and increases reversibility, at theexpense of data loss. However, this data loss can be controlled, asexplained in further detail below.

Anonymization techniques map multiple data items into one; for example,it can replace the date with the month and year only. Thisgeneralization involves a loss of information, but data staysconsistent. K-anonymity aims at mapping at maximum k data items into onerepresenting data item. Anonymization techniques are irreversible, sincethere no way to know the original data item.

GT-NeNDS stands for Geometric Transformation—Nearest Neighbor DataSubstitution. GT techniques include scaling, rotating, and translation,these preserve data characteristics. The NeNDS technique was proposedfor privacy preservation for Clustering Mining applications. It proceedslike this: it clusters the original dataset into sets of neighbors.Neighborhood is determined using Euclidean Distance. Each data item in aneighbors' set is replaced by the nearest neighbor in this set, in a waysuch that no swapping occurs, using special data structures. Thus,statistical properties of the original data are preserved. NeNDSintroduce a degree of obfuscation by replacing a data item with itsnearest neighbor. GT-NeNDS aims at securing the data by furtherobfuscating the nearest neighbor, using the GT techniques.

GT-NeNDS does not adequately fit real-time requirements due to thefollowing reasons. First, to construct the sets of neighbors, thealgorithm needs a pass through all the data, which is not feasible inreal-time settings. Second, substituting a data item with its nearestneighbor means that the substitution is not repeatable because neighborschanges with insertions and deletions.

To overcome these shortages, in accordance with an embodiment, aGT-ANeNDS technique, and an extension to GT-NeNDS are disclosed herein.

System Architecture

FIG. 1 illustrates an environment or system which can use dataobfuscation, in accordance with an embodiment. As shown in FIG. 1, thesystem 102 can be implemented with or as part of a software-based datareplication product, such as Oracle GoldenGate (and which, in accordancewith an embodiment, is referred to herein as BronzeGate). The systemincludes a userExit process 103, which performs user defined customizedtransformations to the replicated transactions. The system (i.e.BronzeGate) is hence a special type of userExit process, where the taskis to perform the required obfuscation on the fly.

As further shown in FIG. 1, the process can be configured to run at anoriginal database site 116, to obfuscate the transactional data beforethey are shipped to a replicate site, for example as part of a trailinformation 120, such as a GoldenGate trail file. The process isactivated by the capture process 118, which monitors the originaldatabase. Whenever a transaction is committed to the original database,the capture process will capture this change and signals the userExit(BronzeGate) process to handle this transaction. The system then usesthe parameters file 108, histograms 122, 124, and dictionaries 110, 112to obfuscate the new transaction. Once done, the system sends theobfuscated transaction back to the capture process which simply writesit to the trail, which shall be shipped to the replication site.

In accordance with an embodiment a GT-ANeNDS technique is proposedherein, which overcomes GT-NeNDS' real-time limitations, and leveragethe level of data privacy.

FIG. 2 illustrates the use of a GT-ANeNDS algorithm 130, in accordancewith an embodiment. GT-ANeNDS combines anonymization and NeNDStechniques, which yields to gain efficiency, real-time adherence,repeatable mapping, and higher level of data privacy. This comes at theexpense of information loss. However, this loss is controlled so thatthe data usability is not affected. In accordance with an embodiment,GT-ANeNDS can be applied to any data type for which a distance functioncan be defined. The higher level view of the algorithm is provided firstand explained using numerical data type. In the discussion hereafter, bydataset we refer to a field, or a column, in the original databaseschema. FIG. 2 lists the main steps of the GT-ANeNDS approach. The inputto the algorithm consists of the new transactional data item, and themeta-data. The meta-data consists of data-type, histogram and semantics:

-   -   Data-Type: The data-type is the regular database type, i.e.,        numerical, text, timestamp, etc. In addition to the semantics,        datatype is used to determine the technique to use.    -   Histogram: The term histogram is used in a generic way to refer        to the data structure that is incrementally maintained.    -   Semantics: The semantics of each data set is a record of the        following information whenever applicable. Data-Sub-Type: for        numerical data, the sub-type defines whether the data are        general, or identifiable. Where identifiable data can identify        the person, such as the national ID number. Euclidean distance        Function: the function to be used to calculate the Euclidean        distance between two values. The Origin point: the reference        point of this data set.

Given the data-type and the semantics, the appropriate obfuscationtechnique is determined. In case it is GT-ANeNDS, the origin-point andthe Euclidean distance function determine the appropriate bucket in thehistogram, and the nearest neighbor therefore. Next, GT function isapplied to the nearest neighbor, generating the obfuscated value. Next,we illustrate how the GT-ANeNDS works in case of numerical data types.

Numerical Data

FIG. 3 illustrates histogram decomposition 132 for numerical data, inaccordance with an embodiment. For general numerical data (i.e., nonID's such as bank account balance), the system can use equi-widthhistograms that splits the range of the data items distances intoregions of the same width (i.e., range) to define the set of neighbors.Each bucket's 134 range is divided into a set of equi-height sub-buckets136. The bucket's width and the subbucket's height are systemsparameters set by the administrator. Histograms are built by scanningthe current database shot once. The number of neighbors for each bucketdepends on the height of the bucket 138 and the position of theseneighbors depends on the values distribution in this range. Note thatthe horizontal axis is not the data value; however, it is the distancefrom the origin point. The vertical access is the frequency. This isintroduced to be able to identify the nearest neighbor without the needto maintain any summary about the data values within each bucket.

The GT-ANeNDS process proceeds as follows. First, the distance betweenthe original data item's value and the origin point is calculated,determining where in the histogram this data item falls. Second, thenearest neighbor point in the histogram is determined. The neighborsset, is the set of points determining sub-buckets' ranges within thesame bucket this point belongs to. Finally, geometric transformation isapplied to the nearest neighbor, generating the obfuscated value. Adifference between the GT-NeNDS and GT-ANeNDS processes is thatGT-ANeNDS uses a fixed set of neighbors for each bucket, which yields tomap more than one original data value to the same obfuscated value,i.e., Anonymization. By fine tuning the bucket widths and the sub-bucketheights, the statistical characteristics of the original data areminimally impacted.

Boolean Data

In accordance with an embodiment, for Boolean data-type, the sameapproach is used but the process simply uses two buckets only, and nosub-buckets. Therefore, the system can maintain in this case twocounters for each bucket. To obfuscate a value, the new value israndomly drawn with probability to have the same ratio of the twovalues. For example, if it is a Gender field and the counters are: tenfemales and seven males, then the obfuscated value is set to M (i.e.,male) with probability 7/17.

Identifiable Numerical Data

FIG. 4 illustrates a function for obfuscating identifiable numericaldata 140, in accordance with an embodiment. For a numerical value is akey, such as national identification number, anonymization is not validas it will result in distortion of the referential integrityconstraints. In accordance with an embodiment, a Special Function 1illustrated in FIG. 4 can be used. Opposed to NeNDS, the process can usea FaNDS technique (Farthest Neighbor Data Substitution). This is exactlysame as NeNDS except that the process substitutes the data item with itsfarthest neighbor. Each digit of the original value is treated as aseparate value to obfuscate. The set of digits are used as the neighborsfrom which the farthest neighbor is chosen to replace the originaldigit. Next, rotation is applied for each replaced digit and saved in atemporarily variable. This rotated number that results from replacingeach digit in the original key and then rotating it is being added tothe original key value and result is truncated to the key length andsaved in a second temporarily variable. Finally, the obfuscated key isgenerated by randomly picking each digit from the two temporarilyvariables.

Date Data

For date data type, neither GT-ANeNDS nor Special Function 1 fits,because of the semantics of the date. Therefore, in accordance with anembodiment the process can use a Special Function 2, to obfuscated dateand timestamp data types, wherein the function basically utilizescontrolled randomness to obfuscate each component of the date, i.e., theday, month and year.

Other Data Types

FIG. 5 illustrates a table 144 of possible data-types and semantics, andwhich technique the system can use to obfuscate each data type, inaccordance with an embodiment. In accordance with an embodiment, thesystem allows the user to overwrite these default selections and todefine a user-defined obfuscation function. Depending on theimplementation, the metadata about which technique to be used and itsparameters can be stored in the original database itself, or in aparameters file.

Analysis

In the following sections, the degree of data privacy, repeatability,and data usability of the proposed obfuscation techniques is analyzed.

Anonymization generally guarantees securing data 100%. Hence, numericalgeneral data obfuscated using the GT-ANeNDS and that obfuscated using adictionary are guaranteed to secure the privacy. For identifiablenumerical data, Special Function 1 obfuscates the data using twodifferent techniques then randomly picks digits from both obfuscatedvalues into one new output value. Without full knowledge of the originaldata, there is no way to find out from where each digit was picked.Thus, data privacy is maintained, and the proposed obfuscationtechniques are immune even to partial attacks, in which partialknowledge about the original data and/or the obfuscation process areused to reverse engineer a portion of the original data.

The proposed techniques guarantee obfuscation repeatability, i.e.,applying to the same input data results in the same obfuscated datamaintaining referential integrity. In the techniques used, therandomization can be dependant on the original data, i.e. the randomseed is generated using the original data value, thus guaranteeing itsrepeatability.

Data usability is the hardest question to answer for numerical datasince the proposed techniques introduce some anonymization. However,since the system determines the number of neighbors and their distancesfrom the origin based on the number and distribution of data valueswithin this bucket, thus the set of neighbors should be representativeenough that the anonymized data are still useable.

Performance Issues and Experimental Evaluation

In accordance with an embodiment, initial construction of the histogramsand dictionaries is the only offline process within the system.Depending on the application dynamics, this process might need to berepeated, and the database rereplicated. This should be done in anefficient way, minimizing overhead and downtime.

In the following section, some performance results are described toprovide a sense of how different techniques perform, and to demonstratethe data usability.

Data Usability

FIGS. 6 and 7 illustrate the results of the K-mean algorithm on original150 and obfuscated data 152 respectively, in accordance with anembodiment. In a first experiment, the data usability of the system wasdemonstrated by applying K-mean classification algorithm, with k=8,using Weka Software to both the original and obfuscated data andplotting the results. The workload is a dataset of protein data in ARFFformat. For the data obfuscation, the GT-ANeNDS was applied with thetaequal to 45 degrees, origin point was set to the min value found in theoriginal data set, and the histogram parameters were as follows: bucketwidth equals to one fourth of the range of the original data set, andsub-bucket height was set to 25%, so that there are four subbuckets ineach bucket. As can be seen in FIGS. 6 and 7, the classification resultsare almost exactly the same, which demonstrates the data usability ofthe process.

Obfuscation Sample Results

FIG. 8 illustrates examples of original and obfuscated values, inaccordance with an embodiment. In another experiment, an Oracle databasewas replicated to an MSSQL one using the system. One table was createdthat includes all different data types and obfuscated all fields exceptthe notes, to identify the replicated record. The table shows the firstfive tuples, and their obfuscated replicas. As can be seen from thetable, identifiable numerical values (SSN and credit card) areobfuscated using the Special Function 1 into unique (i.e., identifiable)values. The system also updated and deleted tuples as well, and thecorrect replica reflected the updates, showing the repeatability of thetechniques. The table also shows for other data types how obfuscatedvalues secure the original data.

The present invention may be conveniently implemented using one or moreconventional general purpose or specialized digital computer, computingdevice, machine, or microprocessor, including one or more processors,memory and/or computer readable storage media programmed according tothe teachings of the present disclosure. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those skilled in the softwareart.

In some embodiments, the present invention includes a computer programproduct which is a non-transitory storage medium or computer readablemedium (media) having instructions stored thereon/in which can be usedto program a computer to perform any of the processes of the presentinvention. The storage medium can include, but is not limited to, anytype of disk including floppy disks, optical discs, DVD, CD-ROMs,microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs,DRAMs, VRAMs, flash memory devices, magnetic or optical cards,nanosystems (including molecular memory ICs), or any type of media ordevice suitable for storing instructions and/or data.

The foregoing description of the present invention has been provided forthe purposes of illustration and description. It is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Many modifications and variations will be apparent to the practitionerskilled in the art. The embodiments were chosen and described in orderto best explain the principles of the invention and its practicalapplication, thereby enabling others skilled in the art to understandthe invention for various embodiments and with various modificationsthat are suited to the particular use contemplated. It is intended thatthe scope of the invention be defined by the following claims and theirequivalence.

1. A system for providing transactional data privacy while maintainingdata usability, comprising: an obfuscation process that captures datawhile it is being received in the form of data changes at a first orsource system, selects one or more obfuscation techniques to be usedwith the data according to the type of data captured, and obfuscates thedata, using the selected one or more obfuscation techniques, to createan obfuscated data, for use in generating a trail file or otherinformation reflecting the obfuscated data, or applying the data changesto a target or second system.
 2. The system of claim 1, wherein thefirst or source system and the target or second system aretransaction-based systems, and wherein the obfuscation is performedduring a transaction to replicate data changes at the first or sourcesystem in realtime to the target or second system.
 3. The system ofclaim 1, wherein the first or source system includes a first databaseand the target or second system includes a second database, and whereinthe data changes are replicated between the first and second databasesin realtime.
 4. The system of claim 1, wherein different obfuscationfunctions are used for different types of data.
 5. The system of claim1, wherein the obfuscation process is configured according to one ormore parameter files that include dictionary names, and one or morehistograms that are used to classify the data.
 6. The system of claim 1,wherein the obfuscation process is a Geometric Transformation GeometricTransformation—Nearest Neighbor Data Substitution (GT-ANeNDS) processthat combines anonymization and NeNDS features to obfuscate the data. 7.A method for providing transactional data privacy while maintaining datausability, comprising the steps of: capturing data while it is beingreceived in the form of data changes at a first or source system;selecting one or more obfuscation techniques to be used with the dataaccording to the type of data captured; obfuscates the data, using theselected one or more obfuscation techniques, to create an obfuscateddata; and generating a trail file or other information reflecting theobfuscated data, or applying the data changes to a target or secondsystem.
 8. The method of claim 7, wherein the first or source system andthe target or second system are transaction-based systems, and whereinthe obfuscation is performed during a transaction to replicate datachanges at the first or source system in realtime to the target orsecond system.
 9. The method of claim 7, wherein the first or sourcesystem includes a first database and the target or second systemincludes a second database, and wherein the data changes are replicatedbetween the first and second databases in realtime.
 10. The method ofclaim 7, wherein different obfuscation functions are used for differenttypes of data.
 11. The method of claim 7, wherein the obfuscationprocess is configured according to one or more parameter files thatinclude dictionary names, and one or more histograms that are used toclassify the data.
 12. The method of claim 7, wherein the obfuscationprocess is a Geometric Transformation Geometric Transformation—NearestNeighbor Data Substitution (GT-ANeNDS) process that combinesanonymization and NeNDS features to obfuscate the data.
 13. Anon-transitory computer readable medium, including instructions storedthereon which when read and executed by a computer cause the computer toperform the steps comprising: capturing data while it is being receivedin the form of data changes at a first or source system; selecting oneor more obfuscation techniques to be used with the data according to thetype of data captured; obfuscates the data, using the selected one ormore obfuscation techniques, to create an obfuscated data; andgenerating a trail file or other information reflecting the obfuscateddata, or applying the data changes to a target or second system.
 14. Thecomputer readable medium of claim 13, wherein the first or source systemand the target or second system are transaction-based systems, andwherein the obfuscation is performed during a transaction to replicatedata changes at the first or source system in realtime to the target orsecond system.
 15. The computer readable medium of claim 13, wherein thefirst or source system includes a first database and the target orsecond system includes a second database, and wherein the data changesare replicated between the first and second databases in realtime.